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Big Data and AI for Geoscience
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School of Earth Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
School of Artificial Intelligence, China University of Geosciences, Beijing 100083, China
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Big Data and AI for Geoscience

Abstract submission deadline
31 October 2026
Manuscript submission deadline
31 December 2026
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334

Topic Information

Dear Colleagues,

Big data thinking and artificial intelligence (AI) are rapidly reshaping how geoscientists think, analyze, model, and understand the Earth. This Topic explores both the theoretical foundations and practical applications of big data mining algorithms and AI in geoscience. It focuses on new methodological advances, such as knowledge graphs, machine learning, generative models, and foundation models, and their ability to capture spatial, temporal, and multi-scale and multi-modal patterns in geoscientific data. It also highlights data integration, anomaly detection, simulation acceleration and decision-support tools for mineral resource and geo-environmental challenges. This Topic aims to bridge theory and practice. From a theoretical perspective, this Topic will explore the role of big data paradigms in guiding model development, the integration of domain knowledge into AI systems, and the validation of AI methods against physical constraints and geoscientific understanding. On the practical side, this Topic showcases contributions from diverse fields, covering geology, geochemistry, geophysics, mineral exploration, remote sensing, natural hazard prediction, and hydrological forecasting. This Topic also sparks discussions on rigorous, interpretable, and impactful AI, setting the stage for its future in geoscience research.

Prof. Dr. Yongzhang Zhou
Prof. Dr. Hui Yang
Dr. Xiaohui Ji
Topic Editors

Keywords

  • big data mining algorithms
  • machine learning
  • knowledge graph
  • LLM
  • multi-modal data fusion
  • explainable AI
  • computer vision
  • probabilistic forecasting
  • geological modeling
  • geophysical inversion
  • geochemical exploration
  • mineral targeting
  • remote sensing
  • natural hazard prediction
  • hydrological forecasting

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Remote Sensing
remotesensing 		4.1 8.6 2009 24.3 Days CHF 2700 Submit
Minerals
minerals 		2.2 4.4 2011 17.7 Days CHF 2400 Submit
Geosciences
geosciences 		2.1 5.1 2011 23.6 Days CHF 1800 Submit
GeoHazards
geohazards 		1.6 2.2 2020 20.1 Days CHF 1400 Submit
Applied Sciences
applsci 		2.5 5.5 2011 16 Days CHF 2400 Submit

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Published Papers (1 paper)

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Article
New Insights into the Xiongbaxi–Yalongri Cu-W(-Mo) Deposit (Tibet): Scheelite Geochemistry and Machine Learning Constraints on Ore-Forming Fluid Evolution and Genetic Type
by Qinggong Li, Jinshu Zhang, Jianhui Wu, Xiaojia Jiang and Bei Pang
Minerals 2026, 16(2), 217; https://doi.org/10.3390/min16020217 - 20 Feb 2026
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Abstract
The Zhunuo ore district, at the western end of the Gangdese porphyry Cu belt, hosts significant Cu mineralization and newly recognized W mineralization dominated by scheelite. However, the genetic relationship between scheelite and porphyry mineralization, and the evolution of ore-forming fluids remain poorly [...] Read more.
The Zhunuo ore district, at the western end of the Gangdese porphyry Cu belt, hosts significant Cu mineralization and newly recognized W mineralization dominated by scheelite. However, the genetic relationship between scheelite and porphyry mineralization, and the evolution of ore-forming fluids remain poorly constrained. To address this, scheelite samples from multiple locations were analyzed for major elements (EMPA), in situ trace elements (LA-ICP-MS), and internal textures (cathodoluminescence, CL). These data, combined with machine learning methods, were used to determine scheelite genetic types and reconstruct fluid evolution. REE patterns and CL textures reveal three scheelite generations in Yalongri (early Sch I c, middle Sch I b, late Sch I a), two in Zhigunong (early Sch II a, late Sch II b), and one in Xiongbaxi (Sch III). Low Na (0–329 ppm) and Nb (3.9–39 ppm) relative to high ΣREE + Y-Eu (16–3857 ppm), indicate that the dominant substitution mechanism is 3Ca2+ = 2REE3+ + □Ca (□Ca = Ca vacancy). δEu values > 1 in Sch I a, Sch I b, Sch II a, and Sch II b indicate reducing fluids, whereas δEu < in Sch I c and Sch III reflects oxidizing conditions. Variations in REE, Mo, and Sr contents suggest that ore-forming fluids in Yalongri evolved from oxidizing to reducing conditions, with late-stage scheelite undergoing dissolution–reprecipitation. Zhigunong records two reducing stages: an early REE-rich-Mo-poor stage and a later REE-poor-Mo-rich stage. Xiongbaxi records a single oxidizing, REE-rich, Mo-rich stage. Scheelite exhibits low-to-moderate Sr/Mo ratios (0.02–6.10), consistent with a magmatic–hydrothermal origin, and relatively uniform Y/Ho ratios (12–59) indicating stable crystallization conditions. A Random Forest model classifies scheelite into orogenic, porphyry, skarn, and greisen types. Overall, the results indicate that ore-forming fluids evolved from oxidizing to reducing conditions, favoring metal transport and enrichment. Integrated geochemical and machine learning evidence suggest, strong potential for porphyry-type Cu-W(-Mo) mineralization in Yalongri and Zhigunong, and skarn-type W-Mo mineralization in Xiongbaxi, providing important guidance for future exploration in the western Gangdese metallogenic belt. Full article
(This article belongs to the Topic Big Data and AI for Geoscience)
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